Search results for "Distributed Optimization and Learning"
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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles
This book gives an exposition of recently developed approximate dynamic programming (ADP) techniques for decision and control in human engineered systems. ADP is a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems. It is connected from a theoretical point of view with both adaptive control and optimal control methods. The book shows how ADP can be used to design a family of adaptive optimal control algorithms that converge in real-time to optimal control solutions by measuring data along the system trajectories. Generally, in the current literature adaptive controllers and optimal controllers are two distinct methods for the design of automatic control systems. Traditional adaptive controllers learn online in real time how to control systems, but do not yield optimal performance. On the other hand, traditional optimal controllers must be designed offline using full knowledge of the systems dynamics. It is also shown how to use ADP methods to solve multi-player differential games online. Differential games have been shown to be important in H-infinity robust control for disturbance rejection, and in coordinating activities among multiple agents in networked teams. The focus of this book is on continuous-time systems, whose dynamical models can be derived directly from physical principles based on Hamiltonian or Lagrangian dynamics.
R 6,126.00
Frequency-Domain Analysis and Design of Distributed Control Systems
This book presents a unified frequency-domain method for the analysis of distributed control systems. The following important topics are discussed by using the proposed frequency-domain method: (1) Scalable stability criteria of networks of distributed control systems; (2) Effect of heterogeneous delays on the stability of a network of distributed control system; (3) Stability of Internet congestion control algorithms; and (4) Consensus in multi-agent systems. This book is ideal for graduate students in control, networking and robotics, as well as researchers in the fields of control theory and networking who are interested in learning and applying distributed control algorithms or frequency-domain analysis methods.
R 5,306.00
Large Scale Systems: Decentralization, Structure Constraints, and Fixed Modes
This book is devoted to Large Scale Systems methodologies including decomposition, aggregation, and model reduction techniques. The focus is put on theoretical and practical results resulting from the application of these techniques in the area of stability and decentralized control. Every result is illustrated by examples to facilitate understanding. The appendices provide a collection of ready-to-use packages implementing some algorithms included in the book. Graduate students concerned with system and control theory will be interested in this book, since it offers a global synthesis on the problem of structurally constrained control. The book addresses also scientists and lecturers in the areas of large scale systems and control theory.
R 1,712.75
